Ablation Study: We have done a thorough ablative study to justify the contribution of every design choice on both IAM (HTR) and IC15 (STR) datasets using Shi [1]} as baseline.
[i] Significance of auxiliary decoder: On removing this part along with its respective loss function from Eqn. REF  we see a performance drop of 2.19%(1.98%) and 5.01%(2.94%) in the CS and DS setting for IAM (IC15) dataset respectively, thus confirming its contribution.
[ii] Significance of monotonically decreasing constraint: Removing it, destabilizes by 5.47% (3.87%) in DS setup, for IAM (IC15) datasets respectively, thus confirming its importance. [iii] Significance of using error distribution: Discarding data inclusion from \(\mathcal {G}\) (error distribution), leads to a performance drop of 2.17% (1.87%) in conventional train-test split, and a further drop of 4.9% (2.48%) in disjoint setup, for IAM (IC15) datasets. Please refer to Table REF  for more analysis. [iv] Finding optimal block for feedback: We evaluated the performance by providing a feedback signal into every ResNet block of backbone feature extractor of \(\mathbf {T}\)  at a time. Table REF  shows a complete analysis on both IAM and IC15 datasets in CS and DS setups which shows the optimum result to be obtained by supplying feedback signal to Block-3 of ResNet convolutional architecture. Moreover, local transformation is seen to outperform the global one. [v] Performance with varying text length It is often observed that any text recognition framework struggles to recognize lengthy words. Owing to its iterative refining approach, along with a modelled linguistic prior, our method shows a considerably higher performance in comparison to no-feedback baseline on increasing character sequence length, as shown in Figure REF . [vi] Computational cost: Finally, we want to notify that benefit of any iterative pipeline [2]}, [3]}, [4]} does incur extra computational expenses, be it text rectification [5]} or in our case text-recognition. A through study on complexity and speed analysis (Intel(R) Xeon(R) W-2123 CPU @ 3.60GHz) in Table REF  reveals that \(T_{B}\)  takes most time due to its sequential decoding operation, whereas \(F\)  adds minimal burden. Please refer to supplementary as well.
